Desarollo de un sistema interactivo para estimulación cognitiva en HTML5

En España hay más de 115.500 personas que padecen Parkinson. Esto la convierte en la segunda enfermedad neurodegenerativa más común, por detrás del Alzheimer. La mayoría de los enfermos se encuentran en edades comprendidas entre los 50 y los 80 años, lo que unido al incremento de la esperanza de vida hace que se prevea un incremento del número de enfermos de Parkinson en pocos años. El Parkinson es un desorden crónico y degenerativo que afecta a la parte del cerebro encargada del sistema motor, es decir, la encargada de coordinar la actividad, el tono muscular y los movimientos, así como a las capacidades cognitivas. Esta patología crónica, de momento, no tiene cura. A los pacientes se les aplican tratamientos farmacológicos para frenar la progresión de la enfermedad. Además, se aplican terapias adicionales como la fisioterapia, la logopedia, la musicoterapia, la estimulación cognitiva o la terapia ocupacional. El uso de las Tecnologías de la Información y Comunicaciones en el campo de la estimulación cognitiva permite que personas con deterioro cognitivo puedan realizar sesiones de estimulación desde su domicilio de forma remota, complementando las terapias individuales y/o grupales que haya indicado el terapeuta. Además, evita desplazamientos hasta el centro de atención, que en ocasiones pueden ser difíciles de efectuar por encontrarse en lugares alejados o por problemas de movilidad del afectado. Asimismo, el uso de este tipo tecnología permite que los resultados de los ejercicios realizados por los pacientes se puedan almacenar para que el terapeuta los pueda analizar en cualquier momento y de esta manera ir adecuando la terapia. Finalmente, la plataforma que se propone cuenta con el valor añadido de permitir la interactividad con los terapeutas y la posibilidad de adaptar los ejercicios a cada paciente, según las necesidades que presente cada uno. SUMMARY. In Spain, there are more than 115.500 people with Parkinson disease. Due to this, it is the second most common neurodegenerative disease, only behind Alzheimer's disease. Most patients have ages between 50 and 80 years of age, which together with the increase in life expectancy to provide an increase in the number of patients with Parkinson's in a few years. Most patients have aged between 50 and 80 years old, which together with the increase of life expectancy provide a growth in the number of people with Parkinson’s in a few years. Parkinson's is a chronic and degenerative disorder that affects the part of the brain responsible for the motor system, i.e., responsible for coordinating activity, muscle tone and movements, as well as cognitive abilities. Nowadays, this chronic pathology has no cure. Pharmacological treatments are applied to patients for slowing down the advance of this disease. In addition, there are additional therapies such as physiotherapy, speech therapy, music therapy, cognitive stimulation or occupational therapy. The use of the Information Technologies and Communications in the field of cognitive stimulation allows people with cognitive impairment may carry out stimulation sessions in their home remotely, complementing individual therapies or group therapies provided by the therapist. This minimizes trips to the attention center, which sometimes can be difficult due to they live in remote places or they are mobility-reduced people. In addition, the use of such technology allows that the results of the exercises personalized by patients can store so that the therapist can analyze them at any time and therefore he or she adapts the therapy. Finally, the proposed platform brings the added value of allowing interaction with the therapists and the possibility of adapting the exercises to each patient according to his or her needs.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

A telehealth system for Parkinson's disease remote monitoring. The PERFORM approach.

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson's disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson's disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability.

A telehealth system for Parkinson's disease remote monitoring. The PERFORM approach

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson s disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson s disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability.

Preliminary results of ON/OFF detection using an integrated system for Parkinson's disease monitoring

This paper describes the experimental set up of a system composed by a set of wearable sensors devices for the recording of the motion signals and software algorithms for the signal analysis. This system is able to automatically detect and assess the severity of bradykinesia, tremor, dyskinesia and akinesia motor symptoms. Based on the assessment of the akinesia, the ON-OFF status of the patient is determined for each moment. The assessment performed through the automatic evaluation of the akinesia is compared with the status reported by the patients in their diaries. Preliminary results with a total recording period of 32 hours with two PD patients are presented, where a good correspondence (88.2 +/- 3.7 %) was observed. Best (93.7 por ciento) and worst (87 por ciento) correlation results are illustrated, together with the analysis of the automatic assessment of the akinesia symptom leading to the status determination. The results obtained are promising, and if confirmed with further data, this automatic assessment of PD motor symptoms will lead to a better adjustment of medication dosages and timing, cost savings and an improved quality of life of the patients.

Predicting EQ-5D from the Parkinson's disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers

The impact of the Parkinson's disease and its treatment on the patients' health-related quality of life can be estimated either by means of generic measures such as the european quality of Life-5 Dimensions (EQ-5D) or specific measures such as the 8-item Parkinson's disease questionnaire (PDQ-8). In clinical studies, PDQ-8 could be used in detriment of EQ-5D due to the lack of resources, time or clinical interest in generic measures. Nevertheless, PDQ-8 cannot be applied in cost-effectiveness analyses which require generic measures and quantitative utility scores, such as EQ-5D. To deal with this problem, a commonly used solution is the prediction of EQ-5D from PDQ-8. In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classifiers. Our approach is evaluated using five-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches, two commonly used mapping methods namely, ordinary least squares and censored least absolute deviations, and a deterministic model. Experimental results are promising in terms of predictive performance as well as the identification of dependence relationships among EQ-5D and PDQ-8 items that the mapping approaches are unable to detect